High accuracy FPGA activation function implementation for neural networks

被引：21
|
作者：
Hajduk, Zbigniew [1 ]
机构：
[1] Rzeszow Univ Technol, Ul Powstancow Warszawy 12, PL-35959 Rzeszow, Poland
来源：
NEUROCOMPUTING | 2017年 / 247卷
关键词：
FPGA; Hyperbolic tangent; Sigmoid; Floating point arithmetic; HARDWARE IMPLEMENTATION;
D O I：
10.1016/j.neucom.2017.03.044
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This letter shortly presents an FPGA implementation method of the hyperbolic tangent and sigmoid activation functions for artificial neural networks. A kind of a direct implementation of the functions is proposed. The implementation results show that the obtained accuracy of the method is relatively high compared to other published solutions. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：59 / 61
页数：3
相关论文
共 50 条
  • [1] Efficient Implementation of Activation Function on FPGA for Accelerating Neural Networks
    Qian, Kai
    Liu, Yinqiu
    Zhang, Zexu
    Wang, Kun
    2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS, 2023,
  • [2] Accelerating the Activation Function Selection for Hybrid Deep Neural Networks - FPGA Implementation
    Waseem, Shaik Mohammed
    Suraj, Alavala Venkata
    Roy, Subir Kumar
    2021 IEEE REGION 10 SYMPOSIUM (TENSYMP), 2021,
  • [3] FPGA Realization of Activation Function for Artificial Neural Networks
    Saichand, Venakata
    Nirmala, Devi M.
    Arumugam., S.
    Mohankumar, N.
    ISDA 2008: EIGHTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 3, PROCEEDINGS, 2008, : 159 - 164
  • [4] FPGA Implementation of Function Approximation Module for Artificial Neural Networks
    Bohrn, Marek
    Fujcik, Lukas
    Vrba, Radimir
    TSP 2010: 33RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING, 2010, : 142 - 145
  • [5] Implementation of High Accuracy Trigonometric Function on FPGA by Taylor Expansion
    Zhang, Duoli
    Yu, Jingju
    Song, Yukun
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE & APPLICATION TECHNOLOGY (ICCIA 2016), 2016, 56 : 175 - 179
  • [6] High precision FPGA implementation of neural network activation functions
    Ortega-Zamorano, Francisco
    Jerez, Jose M.
    Juarez, Gustavo
    Perez, Jorge O.
    Franco, Leonardo
    2014 IEEE SYMPOSIUM ON INTELLIGENT EMBEDDED SYSTEMS (IES), 2014, : 55 - 60
  • [7] Stochastic Implementation of the Activation Function for Artificial Neural Networks
    Yeo, Injune
    Gi, Sang-gyun
    Lee, Byung-geun
    Chu, Myonglae
    PROCEEDINGS OF 2016 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS), 2016, : 440 - 443
  • [8] A Quantum Activation Function for Neural Networks: Proposal and Implementation
    Kumar, Saurabh
    Dangwal, Siddharth
    Adhikary, Soumik
    Bhowmik, Debanjan
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [9] Abstraction in FPGA implementation of neural networks
    Ogrenci, Arif Selcuk
    PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON NEURAL NETWORKS (NN' 08): ADVANCED TOPICS ON NEURAL NETWORKS, 2008, : 221 - 224
  • [10] Reconfigurable FPGA implementation of neural networks
    Hajduk, Zbigniew
    NEUROCOMPUTING, 2018, 308 : 227 - 234
12345